AIGigLab MCP Server

generate_thumbnail_image

Generate a platform-specific thumbnail image for your video or post by providing a visual scene description and viral concept type.

Instructions

Generate a real thumbnail image based on a selected concept.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "conceptType": "<conceptType>",
      "visualDescription": "<visualDescription>"
    },
    "name": "generate_thumbnail_image"
  }
}

Input Schema

Name	Required	Description	Default
`style`	No	Artistic style (e.g., 'hyper-realistic', '3d render')
`format`	No	Target platform format	youtube_long
`videoTitle`	No	The video title (for history tracking)
`conceptType`	Yes	The type of viral angle
`visualDescription`	Yes	Detailed visual description of the thumbnail scene

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.3.0Jul 15, 2026

TDQS

C2.9/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, and the description does not disclose whether the tool creates a new asset, overwrites existing ones, or requires specific permissions. The phrase 'Generate a real thumbnail image' implies file creation but lacks detail on side effects or constraints.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Single sentence, no extraneous words. Efficient but could incorporate more context about parameters or output without bloating.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema, and the description fails to mention the return value (e.g., an image URL or file path). For a generative tool, specifying the output format is critical for proper integration.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the schema already documents parameters. The description adds minimal value beyond restating the overall purpose; it does not elaborate on parameter usage or provide examples.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool generates a real thumbnail image from a selected concept, distinguishing it from sibling tools like generate_thumbnail_concepts. However, it does not explicitly mention key parameters like style or format, which could clarify the scope.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives. There is no mention of prerequisites, such as first generating a concept, or comparison to sibling tools like generate_thumbnail_concepts.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.